In this paper, the possibilities of modern 3D data evaluation for metrology and quality assurance are presented for the special application of the plastic laser sinter process, especially the Additive Manufacturing process. We use the advantages of computer tomography and of the 3D focus variation at all stages of a production process for an increased quality of the resulting products. With the CT and the 3D focus variation the modern quality assurance and metrology have state of the art instruments that allow non-destructive, complete and accurate measuring of parts. Therefore, these metrological methods can be used in many stages of the product development process for non-destructive quality control. In this work, studies and evaluation of 3D data and the conclusions for relevant quality criteria are presented. Additionally, new developments and implementations for adapting the evaluation results for quality prediction, comparison and for correction are described to show how an adequate process control can be achieved with the help of modern 3D metrology techniques. The focus is on the optimization of laser sintering components with regard to their quality requirements so that the functionality during production can be guaranteed and quantified.
KEYWORDS: 3D modeling, 3D image processing, Computed tomography, Clouds, Inspection, Image processing, 3D metrology, Image segmentation, Data modeling, Algorithm development
In recent years the requirements of industrial applications relating to image processing have significantly increased.
According to fast and modern production processes and optimized manufacturing of high quality products, new ways of
image acquisition and analysis are needed. Here the industrial computer tomography (CT) as a non-destructive
technology for 3D data generation meets this challenge by offering the possibility of complete inspection of complex
industrial parts with all outer and inner geometric features. Consequently CT technology is well suited for different kinds
of industrial image-based applications in the field of quality assurance like material testing or first article inspection.
Moreover surface reconstruction and reverse engineering applications will benefit from CT. In this paper our new
methods for efficient 3D CT-image processing are presented. This includes improved solutions for 3D surface
reconstruction, innovative approaches of CAD-based segmentation in the CT volume data and the automatic geometric
feature detection in complex parts. However the aspect of accuracy is essential in the field of metrology. In order to
enhance precision the CT sensor can be combined with other, more accurate sensor systems generating measure points
for CT data correction. All algorithms are applied to real data sets in order to demonstrate our tools.
As nowadays the industry aims at fast and high quality product development and manufacturing processes a modern and
efficient quality inspection is essential. Compared to conventional measurement technologies, industrial computer
tomography (CT) is a non-destructive technology for 3D-image data acquisition which helps to overcome their
disadvantages by offering the possibility to scan complex parts with all outer and inner geometric features. In this paper
new and optimized methods for 3D image processing, including innovative ways of surface reconstruction and automatic
geometric feature detection of complex components, are presented, especially our work of developing smart online data
processing and data handling methods, with an integrated intelligent online mesh reduction. Hereby the processing of
huge and high resolution data sets is guaranteed. Besides, new approaches for surface reconstruction and segmentation
based on statistical methods are demonstrated. On the extracted 3D point cloud or surface triangulation automated and
precise algorithms for geometric inspection are deployed. All algorithms are applied to different real data sets generated
by computer tomography in order to demonstrate the capabilities of the new tools. Since CT is an emerging technology
for non-destructive testing and inspection more and more industrial application fields will use and profit from this new
technology.
The manual segmentation and analysis of 4D high resolution multi slice cardiac CT datasets is both labor
intensive and time consuming. Therefore, it is necessary to supply the cardiologist with powerful software tools,
to segment the myocardium and the cardiac cavities in all cardiac phases and to compute the relevant diagnostic
parameters.
In recent years there have been several publications concerning the segmentation and analysis of the left
ventricle (LV) and myocardium for a single phase or for the diagnostically most relevant phases, the enddiastole
(ED) and the endsystole (ES). However, for a complete diagnosis and especially of wall motion abnormalities, it
is necessary to analyze not only the motion endpoints ED and ES, but also all phases in-between.
In this paper a novel approach for the 4D segmentation of the left ventricle in cardiac-CT-data is presented.
The segmentation of the 4D data is divided into a first part, which segments the motion endpoints of the cardiac
cycle ED and ES and a second part, which segments all phases in-between. The first part is based on a bi-temporal
statistical shape model of the left ventricle. The second part uses a novel approach based on the
individual volume curve for the interpolation between ED and ES and afterwards an active contour algorithm
for the final segmentation.
The volume curve based interpolation step allows the constraint of the subsequent segmentation of the phases
between ED and ES to very small search-intervals, hence makes the segmentation process faster and more robust.
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